GEOG 463 Test 1

Euclidean distance: goes through shortest route
Cell distance: shortest between cells while still going through the center of cells

Contains info that defines the properties of the raster such as cell size, num of rows/col, and coordinates of original raster

run length
Takes advantage of patterns in the data, taking advantage of the repetition of values in a row

Block length encoding:
Uses squares to tile area
Uses block size and then its coordinates

Chain encoding
Follows boundaries using NESW

Quadtree
Recursively divides an area into four quadrants until all the quadrants (at all levels) are homogeneous

1. State the problem/Objective
2. Establish criteria: factors and constraints
3. Standardize factors
4. Establish factor weights
5. Aggregate the criteria
6. validate/verify the results

Factor = criterion that enhances or lowers the suitability of a specific alternative for the activity under consideration e.g. distance to road (near = most suitable; far = least suitable)

Constraint = criterion that limits the alternatives under consideration e.g. water bodies, protected areas, forest areas.

A matrix is constructed, where each criterion is compared with the other criteria, relative to its importance, on a scale from 1 to 9, where 1 = equal preference between two factors; 9 = a particular factor is extremely favored over the other.
A weight estimate is calculated & used to derive a consistency ratio (CR) of the pairwise comparisons.

If CR > 0.10, then some pairwise values need to be reconsidered & the process is repeated until the desired value of CR < 0.10 is reached.

Increasing linear function
u= (x – point a) / (point b – point a) * range
Decreasing linear function
u = {1 – [(x – point c) / (point d – point c)]} * range

a is the value where the membership function starts ( u =0)
b is the value where the membership function ends (u = 1)
u varies between 0 and 1

binary Overlay analysis (Boolean approach):
Limitations:
If no location meets all criteria, there is no second best option identified.
There is no relative weighting of the locations that meet the criteria.

Weighted Overlay analysis
The cell locations with the highest summed values are the most preferred relative to the input criteria.

The mathematical or physical representation of the earth’s surface → Geographic Coordinates:

Geoid: a close representation or physical model of the figure of the Earth
Spheroid (Mathematical)
Ellipsoid / Datum (Mathematic)

The mathematical transformation to convert a 3-D mathematicaleometric shape (Spheroid, Ellipsoid) into a 2-D shape (map) → Projected Coordinates
Convert a 3D (spherical) coordinate system into a 2D (planar) coordinate system

A datum is a reference ellipsoid together with an offset from the center of the Earth.
By specifying different offsets, we can use the same standard ellipsoids in many different regions of the Earth.
Different countries will often use the same ellipsoid but with different offsets/datums
A datum roughly approximates the surface of the earth at some mean sea level value

NAD 27 (ft) (North American Datum in 1927), State plane systems (based on Clarke 1866 ellipsoid)
NAD 83 (m) used for topo maps in North America including Canada and Quebec (based on GRS 80: Geodesic Reference System measured accepted internationally in 1980) - determined from satellite orbital (more accurate); reference: the center of earth.
WGS72 & WGS84 (m) (World Geodetic System) is now the more common reference (GPS ref.) (based on GRS 80)

One ellipsoid can be used for several datums (e.g. GRS 1980)
A reference ellipsoid can also be a datum (e.g. WGS84)
Lat/long coordinates calculated with one datum are valid only with reference to that datum (e.g. Coordinates calculated with NAD 27 are not valid with NAD 83)
To be viewed in another datum, their position must be recalculated (“reprojected”) and they will be given new coordinates

shape - conformal
Area: equivalent or equal area
Distance- equidistant
Direction - azimuthal
Most of everything

Describes how the developable surface is place relative to the globe, it may be
Normal or equatorial (such as the surface axis of symmetry coincides with the earth's axis)
Transverse (at right angles of the earth's axis)
Oblique (any angle in between)

1772
Minimizes distortion in narrow strips running from pole to pole
Divides earth into 60 zones- each zone 500,000 m

Properties:
Shape are preserved in small areas
Area- Minimal distortion of larger shapes occurs within the same zone
Distance: scale is constant along the central meridian
Direction- local angles are true

Limitations:
Error and distortion increase for regions that span more than one UTM zone. UTM is not designed for areas that span more than a few zones
Montreal zone 8

Used in quebec and atlantic provinces
Based on UTM provinces debided into 8 zones of 3º of longitude each
MTM: montreal zone 8

Albers conic equal-area: goof for canada or province
Lambert conformal conic: good for north america, canada (when the area is across several UTM)
UTM good for small areas, small extents

Combine Map Algebra operators
Most common language in the domain of databases
Powerful language to isolate, count and locate individual
Generate alphanumerical AND geographical requests

SQL is the standardized method of interacting with a database

Use SQL to
Insert (new records into a DBMS)
Update (existing records in DBMS)
Delete (remove records from DBMS)
Select (select a subset of records)
Where (limits your results)

In ArcGIS use quotation mark (“) for field names, apostrophe for text (‘) and nothing for numbers

If you are querying a shapefile:
'_' indicates one character.
'%' indicates any number of characters.
[STATE_NAME] LIKE 'Miss%'
[OWNER_NAME] LIKE ’_arina smith‘
(You are not sure if the name is written with C or K)

If you are querying a geodatabase:
'?' indicates one character.
'*' indicates any number of characters.

It is the process of converting addresses into geographic coordinates, which you can use to place on a map

Process
​​1.Tabular data with Lat Lon coordinates
2.Tabular data with no coordinates, but with column that connects to spatial dataset
3.Tabular data with addresses

Geocoding by street address is the process of finding the location of a street address on a map. This involves matching addresses in the record to the street names and numbers in the reference street network file.
Cleans up addresses
Gets rid of unit numbers

linear uses exact distances

everse geocoding is the process of converting coordinates into a human-readbale address

allows to organize, update and interrogate data (e.g.)
is a collection of tables (relations)
Has tables that are defined for each class of objects
Each table can be managed separately from other tables, but can be connected when needed (e.g. for queries)

is the process of organizing the columns (attributes) and tables of a relational database to minimize data redundancy.

Norm 1
Eliminate repeating groups in individual tables.
Create a separate table for each set of related data.
Identify each set of related data with a primary key

A network is a set of interconnected linear features through which materials, goods and people are transported or along which communication of information is achieved (Heywood et al. 2011: 95)

System of interconnected lines (edges) and intersections (junctions) that represent possible routes from one location to another:

Human made networks:
Transport: roads, railways
Service: gas, electricity, telephone lines, water, sewer

Natural networks:
Rivers

transportation/ undirected
Roads, Rails, Subways, Pedestrian networks that can be travelled in any directions (even if legally not for everyone, but possible)

Geometric utility / directed
Water, Sewers, Natural Gas, Electrical Networks are networks that only flow in one direction often due to natural forces.

Edges (lines) — Connect to other elements (junctions) and are the links over which agents travel
Junctions (points) —Connect edges and facilitate navigation from one edge to another
Turns—Store information that can affect movement between two or more edges

Turn impedance: required time to complete the turn (combining several factors, such as traffic lights, stop sign, turning angle, etc.)

Link Impedance: the cost to cross a line segment (roads, most of the time, but could also be pipelines, electrical wires, etc.).
Physical length: actual length of a line segment
Travel time: estimation from the length and a speed limit (driving, biking, walking)

path with the minimum cumulative impedance between nodes. It is an iterative process: every possibility is evaluated.

Edges (lines)—An edge is a feature that has a length through which some commodity flows

Junctions (Points)—A junction is a feature that allows two or more edges to connect and facilitates the transfer of flow and resources between edges.

Sources and Sink (Points) - Sources can be thought of as input whereas sinks can be thought of as Outputs of the system.

Ancillary Features (Points and Areas) – Often nearby elements that are closely related to directed network

Utility networks, such as water distribution or electricity network (or even water network) have

simple random sampling
Systematic sampling
Systematic sampling with local random allocation
Contour sampling/adaptive sampling

Density of sampling points must be chosen to reflect the scale of variation of phenomenon you are measuring

Procedure for estimating attribute values at unsampled sites from known measurements
Can be seen as a type of data transformation (usuallu point → surface)
Geostatistical is based on the assumption of “spatial autocorrelation”

Conventional statistics does not consider spatial variation
2 datasets may looks the same in stats
but look completely different spatially
Determine the ‘nature’ of spatial variations- and quantify the → spatial auto correlation
Estimate the error of a spatial interpolation
Determine an optimal sampling density

Tobler’s 1st law
“Everything is related to everything else, but near things are more related than others”

Morans I
Positive: when nearby areas are simil.ar in attributes
Negative: when nearby areas are more dissimilar
Zero: attributes randomly arranged

Exact: interpolated surface passes through ALL original data points (reproduces data values)
Inexact- does NOT pass through all data points (does not reproduce original data values)

Global: estimated value is a function of all the values in the data set (usually based on statistics- variance and regression)
Local: estimated values is a function of a subset of values within the vicinity of the unknown value (more consistent with toblers law)

Entails fitting mathematical models through points to generate a smooth surface
Spatial coordinates (x and y) are independent variables, and the attribute value is teh dependent variable

rend interpolation results in a smooth surface that represents gradual trends in the surface over an area of interest. This type of interpolation can be used for:
Fitting a surface to the sample points when the surface varies gradually from geion to region

Examining or removing the effects of long-range or global trends. In such circumstances the technique is otehn referred to as trend surface analysis

Trend interpolation creates a gradually varying surface using low-order polynomials that describe a physical process

Attribute of interest very sparsely sampled

Identity a secondary attribute that is very densely sampled

Predict the values at un-sampled locations for the sparse attribute based on a regression model (co-incident points) developed between the two

1. Create TIN that meets Delaunay criterio
The perpendicular bisectors for each triangle edge are generated forming the edges of the Tiessen plygon. The locations at which the bisectors intersect determine the locations of the thiessen polygon vertices

Created by
Subdividing lines joining nearest neighbour points
Drawing perpendicular bisectors through these lines
Assembling polygon edges using these bisectors

Assign the value of the nearest seed point for any unsampled location in an area
Divide a region based on the spatial configuration of the data points
Assume homogeneity of the values within polygons boundaries
Most common use is to establish area territories for a set of points
Area of influence around population centers
Not very appropriate for elevation- abrupt change

Combine idea of proximity from Thiessen with the gradual change of trend surface
Main idea- value of an attribute at an un-sampled location is inversely proportional to its distance from the estimated value

1. For every point to be estimated, the distance to every known data point is determined
2. Inverse, inverse square, or inverse cubed is computed (value of r, the power parameter)
3.Each known point is multiplied by its inverse square distance term and values summed
4. Interpolated value= sum of above divided by sum of inverse square distance

Good:
Can alter search criteria at will
Is frequently used (though this is not necessarily a good thing)

Bad:
sensitive to outliers
Sensitive to clustering producing weird patterns
Has no built in method to test quality of predictions
Inverse distance is somewhat arbitrary in choice of exponents (r-value)

Fits a piece-wise polynomial function to a neighbourhood of sample points
Typically produces a smooth surface

Good
Can be used to smooth surfaces where artefacts (sharp boundaries) appear
Aesthetically pleasing surfaces
Good for analysis of surface geometery/topology (deruvatuves can be computed)

Bad
No easy way to evaluate efficacy of splines
Represents unrealistically smooth version of reality

You need to compare your interpolated map against some sort of standard
You can do this visually or statistically